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Comparing efficacies of moxifloxacin, levofloxacin and gatifloxacin in tuberculosis granulomas using a multi-scale systems pharmacology approach.

Abstract Granulomas are complex lung lesions that are the hallmark of tuberculosis (TB). Understanding antibiotic dynamics within lung granulomas will be vital to improving and shortening the long course of TB treatment. Three fluoroquinolones (FQs) are commonly prescribed as part of multi-drug resistant TB therapy: moxifloxacin (MXF), levofloxacin (LVX) or gatifloxacin (GFX). To date, insufficient data are available to support selection of one FQ over another, or to show that these drugs are clinically equivalent. To predict the efficacy of MXF, LVX and GFX at a single granuloma level, we integrate computational modeling with experimental datasets into a single mechanistic framework, GranSim. GranSim is a hybrid agent-based computational model that simulates granuloma formation and function, FQ plasma and tissue pharmacokinetics and pharmacodynamics and is based on extensive in vitro and in vivo data. We treat in silico granulomas with recommended daily doses of each FQ and compare efficacy by multiple metrics: bacterial load, sterilization rates, early bactericidal activity and efficacy under non-compliance and treatment interruption. GranSim reproduces in vivo plasma pharmacokinetics, spatial and temporal tissue pharmacokinetics and in vitro pharmacodynamics of these FQs. We predict that MXF kills intracellular bacteria more quickly than LVX and GFX due in part to a higher cellular accumulation ratio. We also show that all three FQs struggle to sterilize non-replicating bacteria residing in caseum. This is due to modest drug concentrations inside caseum and high inhibitory concentrations for this bacterial subpopulation. MXF and LVX have higher granuloma sterilization rates compared to GFX; and MXF performs better in a simulated non-compliance or treatment interruption scenario. We conclude that MXF has a small but potentially clinically significant advantage over LVX, as well as LVX over GFX. We illustrate how a systems pharmacology approach combining experimental and computational methods can guide antibiotic selection for TB.
PMID
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Authors

Mayor MeshTerms

Antitubercular Agents

Computer Simulation

Fluoroquinolones

Granuloma

Mycobacterium tuberculosis

Tuberculosis

Keywords
Journal Title plos computational biology
Publication Year Start




PMID- 28817561
OWN - NLM
STAT- MEDLINE
DA  - 20170817
DCOM- 20170828
LR  - 20170828
IS  - 1553-7358 (Electronic)
IS  - 1553-734X (Linking)
VI  - 13
IP  - 8
DP  - 2017 Aug
TI  - Comparing efficacies of moxifloxacin, levofloxacin and gatifloxacin in
      tuberculosis granulomas using a multi-scale systems pharmacology approach.
PG  - e1005650
LID - 10.1371/journal.pcbi.1005650 [doi]
AB  - Granulomas are complex lung lesions that are the hallmark of tuberculosis (TB).
      Understanding antibiotic dynamics within lung granulomas will be vital to
      improving and shortening the long course of TB treatment. Three fluoroquinolones 
      (FQs) are commonly prescribed as part of multi-drug resistant TB therapy:
      moxifloxacin (MXF), levofloxacin (LVX) or gatifloxacin (GFX). To date,
      insufficient data are available to support selection of one FQ over another, or
      to show that these drugs are clinically equivalent. To predict the efficacy of
      MXF, LVX and GFX at a single granuloma level, we integrate computational modeling
      with experimental datasets into a single mechanistic framework, GranSim. GranSim 
      is a hybrid agent-based computational model that simulates granuloma formation
      and function, FQ plasma and tissue pharmacokinetics and pharmacodynamics and is
      based on extensive in vitro and in vivo data. We treat in silico granulomas with 
      recommended daily doses of each FQ and compare efficacy by multiple metrics:
      bacterial load, sterilization rates, early bactericidal activity and efficacy
      under non-compliance and treatment interruption. GranSim reproduces in vivo
      plasma pharmacokinetics, spatial and temporal tissue pharmacokinetics and in
      vitro pharmacodynamics of these FQs. We predict that MXF kills intracellular
      bacteria more quickly than LVX and GFX due in part to a higher cellular
      accumulation ratio. We also show that all three FQs struggle to sterilize
      non-replicating bacteria residing in caseum. This is due to modest drug
      concentrations inside caseum and high inhibitory concentrations for this
      bacterial subpopulation. MXF and LVX have higher granuloma sterilization rates
      compared to GFX; and MXF performs better in a simulated non-compliance or
      treatment interruption scenario. We conclude that MXF has a small but potentially
      clinically significant advantage over LVX, as well as LVX over GFX. We illustrate
      how a systems pharmacology approach combining experimental and computational
      methods can guide antibiotic selection for TB.
FAU - Pienaar, Elsje
AU  - Pienaar E
AUID- ORCID: http://orcid.org/0000-0002-5408-8795
AD  - Department of Chemical Engineering, University of Michigan, Ann Arbor, Michigan, 
      United States of America.
AD  - Department of Microbiology and Immunology, University of Michigan Medical School,
      Ann Arbor, Michigan, United States of America.
FAU - Sarathy, Jansy
AU  - Sarathy J
AD  - Public Health Research Institute and New Jersey Medical School, Rutgers, Newark, 
      New Jersey, United States of America.
FAU - Prideaux, Brendan
AU  - Prideaux B
AD  - Public Health Research Institute and New Jersey Medical School, Rutgers, Newark, 
      New Jersey, United States of America.
FAU - Dietzold, Jillian
AU  - Dietzold J
AD  - Department of Medicine, Division of Infectious Disease, New Jersey Medical
      School, Rutgers University, Newark, New Jersey, United States of America.
FAU - Dartois, Veronique
AU  - Dartois V
AD  - Public Health Research Institute and New Jersey Medical School, Rutgers, Newark, 
      New Jersey, United States of America.
FAU - Kirschner, Denise E
AU  - Kirschner DE
AUID- ORCID: http://orcid.org/0000-0002-1053-2591
AD  - Department of Microbiology and Immunology, University of Michigan Medical School,
      Ann Arbor, Michigan, United States of America.
FAU - Linderman, Jennifer J
AU  - Linderman JJ
AD  - Department of Chemical Engineering, University of Michigan, Ann Arbor, Michigan, 
      United States of America.
LA  - eng
PT  - Journal Article
DEP - 20170817
PL  - United States
TA  - PLoS Comput Biol
JT  - PLoS computational biology
JID - 101238922
RN  - 0 (Antitubercular Agents)
RN  - 0 (Fluoroquinolones)
SB  - IM
MH  - Animals
MH  - *Antitubercular Agents/administration &
      dosage/pharmacokinetics/pharmacology/therapeutic use
MH  - Computational Biology/*methods
MH  - *Computer Simulation
MH  - Female
MH  - *Fluoroquinolones/administration &
      dosage/pharmacokinetics/pharmacology/therapeutic use
MH  - *Granuloma/drug therapy/microbiology
MH  - Humans
MH  - Microbial Sensitivity Tests
MH  - *Mycobacterium tuberculosis/drug effects/pathogenicity
MH  - Rabbits
MH  - *Tuberculosis/drug therapy/microbiology
PMC - PMC5560534
EDAT- 2017/08/18 06:00
MHDA- 2017/08/29 06:00
CRDT- 2017/08/18 06:00
PHST- 2017/01/19 [received]
PHST- 2017/06/26 [accepted]
AID - 10.1371/journal.pcbi.1005650 [doi]
AID - PCOMPBIOL-D-17-00104 [pii]
PST - epublish
SO  - PLoS Comput Biol. 2017 Aug 17;13(8):e1005650. doi: 10.1371/journal.pcbi.1005650. 
      eCollection 2017 Aug.